First-Principles Prediction of the Information Processing Capacity of a Simple Genetic Circuit†
Manuel Razo-Mejia, Sarah Marzen, Griffin Chure, Rachel Taubman, Muir Morrison,
Rob Phillips⛧
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SI
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GitHub Repository
Phys. Rev. E
† Selected as the ``Editor's Suggestion" for the August 2020 issue of Physical Review E
Scientific Abstract
Given the stochastic nature of gene expression, genetically identical cells exposed to the same environmental inputs will produce different outputs. This heterogeneity has been hypothesized to have consequences for how cells are able to survive in changing environments. Recent work has explored the use of information theory as a framework to understand the accuracy with which cells can ascertain the state of their surroundings. Yet the predictive power of these approaches is limited and has not been rigorously tested using precision measurements. To that end, we generate a minimal model for a simple genetic circuit in which all parameter values for the model come from independently published data sets. We then predict the information processing capacity of the genetic circuit for a suite of biophysical parameters such as protein copy number and protein-DNA affinity. We compare these parameter-free predictions with an experimental determination of protein expression distributions and the resulting information processing capacity of E. coli cells. We find that our minimal model captures the scaling of the cell-to-cell variability in the data and the inferred information processing capacity of our simple genetic circuit up to a systematic deviation.